לוח משרות דרושים NLP/Machine Learning משרה מלאה

Join us to build cutting-edge systems, collaborate with world-class engineers, and shape how autonomous software is built in production.
We are looking for an AI Engineer to join the hunt.
Responsibilities:
Take part in the design and development of AI-driven features for our Dev-Native Observability Platform.
Collaborate with cross-functional teams to integrate AI solutions into existing systems.
Contribute to the product roadmap by identifying AI opportunities and providing insights as a potential end user
Stay updated with the latest AI trends and technologies to ensure our platform remains cutting-edge.

Required Senior ML Data Engineer
Jerusalem
Full time
The AI Engineering group builds modern infrastructure and solutions that improve how algorithms are developed.
We are a small, independent team of experienced engineers with a mix of skills in algorithms, software, and infrastructure. We work in a DevOps style and build cross-team solutions that support research and development of advanced perception algorithms.
Our flagship project is a unified AV dataset used to train and evaluate next-generation models. We take large volumes of multi-camera video, object labels, HD maps, and sensor data from across the organization, and turn it into a curated, high-quality training set - at scale.
We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).

Our CTO Group is looking for an outstanding Physical-AI Applied Researcher to join our team.
The CTO Group is a small, elite research unit shaping the next generation of algorithmic foundations behind our autonomous driving systems. The group operates at the core of the decision-making and planning stack, addressing some of the most challenging problems in real-world autonomy.
We are seeking a researcher who thrives at the intersection of machine learning, decision-making, and algorithmic rigor - someone who is excited about advancing learning-based approaches for safety-critical, large-scale physical systems.
In this role, you will develop novel approaches for planning and decision-making in interactive, multi-agent driving environments. You will combine deep & reinforcement learning with classical algorithmic structure and formal reasoning. The problems are open-ended, scientifically challenging, and deployed at unprecedented scale.
This is a rare opportunity to conduct high-impact applied research, taking ideas from theory and papers into real-world autonomous systems at scale. If youre excited about pushing the boundaries of learning-based decision-making, wed love you to join us and help shape the future of Physical AI.
What will your job look like:
Design and develop novel learning-based algorithms for decision-making and planning in complex physical environments.
Advance model architectures for long-horizon reasoning, multi-agent interaction, and uncertainty-aware prediction.
Integrate deep learning components into structured planning pipelines with clear formal objectives and safety constraints.
Formulate problems mathematically and derive principled learning objectives grounded in real-world system requirements.
Lead research directions from conception to full-scale production.
Develop using Python (PyTorch or similar frameworks) as well as C++/GPU/Cuda.

Required Senior ML Data Engineer
Ramat Gan
Full time
The AI Engineering group builds modern infrastructure and solutions that improve how algorithms are developed.
We are a small, independent team of experienced engineers with a mix of skills in algorithms, software, and infrastructure. We work in a DevOps style and build cross-team solutions that support research and development of advanced perception algorithms.
Our flagship project is a unified AV dataset used to train and evaluate next-generation models. We take large volumes of multi-camera video, object labels, HD maps, and sensor data from across the organization, and turn it into a curated, high-quality training set - at scale.
We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).

Our CTO Group is looking for an outstanding Physical-AI Applied Researcher to join our team.
The CTO Group is a small, elite research unit shaping the next generation of algorithmic foundations behind our autonomous driving systems. The group operates at the core of the decision-making and planning stack, addressing some of the most challenging problems in real-world autonomy.
We are seeking a researcher who thrives at the intersection of machine learning, decision-making, and algorithmic rigor - someone who is excited about advancing learning-based approaches for safety-critical, large-scale physical systems.
In this role, you will develop novel approaches for planning and decision-making in interactive, multi-agent driving environments. You will combine deep & reinforcement learning with classical algorithmic structure and formal reasoning. The problems are open-ended, scientifically challenging, and deployed at unprecedented scale.
This is a rare opportunity to conduct high-impact applied research, taking ideas from theory and papers into real-world autonomous systems at scale. If youre excited about pushing the boundaries of learning-based decision-making, wed love you to join us and help shape the future of Physical AI.
What will your job look like:
Design and develop novel learning-based algorithms for decision-making and planning in complex physical environments.
Advance model architectures for long-horizon reasoning, multi-agent interaction, and uncertainty-aware prediction.
Integrate deep learning components into structured planning pipelines with clear formal objectives and safety constraints.
Formulate problems mathematically and derive principled learning objectives grounded in real-world system requirements.
Lead research directions from conception to full-scale production.
Develop using Python (PyTorch or similar frameworks) as well as C++/GPU/Cuda.

Required AI Algorithm Engineer - Localization Team
Ramat Gan
Full time
You will be part of our REM (Road Experience Management) department, which is responsible for the automatic High-Definition map-making process, which is a key technology in our autonomous driving and high-end Driving Assistance systems.
Vision-based localization is a key enabler for utilizing and creating the maps. Our group is responsible for aligning the map with the world, and reporting detected changes during the drive. Our technology is safety-critical, and the code has strict time and memory constraints as it operates in real time.
What will your job look like:
Develop and optimize computer vision and deep learning algorithms to accelerate data generation and labeling workflows for autonomous driving
Apply both classical computer vision techniques and modern deep learning methods to solve large-scale data challenges
Collaborate closely with development and annotation teams to enhance automation and ensure high-quality data
Own the entire lifecycle from prototyping to scalable deployment within internal pipelines and tools
Develop tools to evaluate and analyze algorithm performance, robustness, and operational efficiency.

we are looking for a Senior Data Scientist.
As a Sr. Data Scientist, you will be spearheading the analysis of tens of thousands of patient-therapist conversations, working closely with our leadership, product, and clinical experts team, to derive insights that directly improve therapy sessions for thousands of people.
This is a unique opportunity to join a startup with real impact on the wellbeing and mental health of thousands, applying cutting edge technology for solving a meaningful human problem with huge financial and psychological implications.

we are looking for a ML Engineer.
As a Senior ML Infrastructure Engineer, you will design and build the infrastructure that powers our ML and LLM systems in production. You will develop scalable pipelines, systems, and tools that enable data scientists and AI teams to efficiently develop, test, and deploy models.
Working closely with data scientists, engineers, and product teams, you will ensure our ML capabilities are reliable, scalable, and production-ready-helping bring cutting-edge AI to improve mental health care.
This is a unique opportunity to join a startup with a real impact on thousands of peoples wellbeing and mental health, applying cutting-edge AI technologies to solve meaningful human problems.

We are looking for a Senior Engineer with a data engineering background to join our growing ML Platform team. This is a great opportunity, whether you have experience with ML and are looking for a ML focused product or are an experienced Data Engineer looking to enter the world of ML. Together well provide tools to develop more effective models, get them into production faster, and ensure that they continue to perform well over time.
ML is central to our work. It enables us to process billions of $ worth e-commerce transactions, make decisions in real time, identify fraud rings, and quickly detect new attack methods. Precision is crucial - bad decisions by our models cost us directly and put money into the pockets of fraudsters.
Our adoption by merchants around the world provides us with billions of fresh data points each day. Our team of data scientists, analysts, and cyber intelligence specialists continually identify new signals, engineer new features, and research new models. But as the volume of data and the number and complexity of models grows, so do the engineering challenges.
If this kind of working environment sounds exciting to you, if you understand that Engineering is about building the most effective and elegant solution within a given set of constraints - consider applying for this position.
Why should you join us?
Youll be part of a highly proficient engineering team that is a focal point for all ML engineering activity, striving to constantly bring innovation and leverage ML capabilities across all company teams and products.
This role presents a unique opportunity to enter the ML domain. For those already experienced in ML infrastructure, it offers the chance to grow within a team that specializes in high-scale, Big Data and ML systems.
What you will be doing:
Designing, building, and maintaining the ML infrastructure that allows our models to make billions of real-time decisions every year.
Building a platform that enables managing a full ML model lifecycle - from researching to training, deploying, and serving predictions in real-time.
Building distributed data processing pipelines to support model development.
Acting as a consultant to researchers, data scientists, and expert analysts and enabling them to research new models faster and with greater precision by providing cutting-edge tooling.
Expanding our ML infrastructure to make it scalable, quick, and efficient to bring diverse models to production and to monitor their performance and drift over time.
Expanding the pool of internal customers able to use ML. Work with them to understand their needs and help them make the most of the infrastructure that well provide.
Acting as an advocate for MLOps, continually improving our processes, and raising our standards.

Alice is building its internal AI infrastructure layer from the ground up. We have real agents running in production, a growing base of employees using AI in their daily work, and a clear architectural direction. What we don't have yet is a dedicated engineer to own it. You'll be the first. Your job is to close the gap between "working prototype" and "production platform" - owning the foundation that hosts our agents, the pipelines that ship them, and the reliability layer (observability, cost controls, audit trails, evals) that makes it safe to run AI at scale in a trust & safety company. This is an infrastructure-first role with deep AI fluency - not a prompt engineer, not a wrapper-framework operator, not a no-code builder. You should be equally comfortable writing a Terraform module, debugging a Kubernetes pod, and tracing an agent's tool-call chain. We don’t operate with a predefined backlog here; you will be responsible for identifying high-impact needs and bringing them to life. The perfect fit for this role has a track record of deploying agentic systems that have held up under real-world usage, balances a focus on infrastructure with a deep concern for user experience, and recognizes that the primary hurdle in AI integration is rarely the model itself. Responsibilities: Platform & Infrastructure
* Architect, build, and run the AWS/Kubernetes platform that hosts Alice's internal AI agents and tools; drive AWS Well-Architected pillars (operational excellence, security, reliability, performance, cost, sustainability).
* Own Infrastructure-as-Code: Terraform modules, standards, and reviews for Bedrock, agent runtimes, vector DBs, and supporting services. AI Systems
* Design and ship production-grade agents and multi-agent pipelines using the Anthropic Agent SDK, Claude Code, AWS Bedrock, and MCP — not wrapper frameworks.
* Own the full agent lifecycle: scoping ? prototyping ? eval ? deploy ? monitor ? iterate.
* Integrate agentic workflows into internal and product systems via APIs, databases, webhooks, Slack, and email. Reliability, Observability, Cost
* Build first-class observability across apps and infra: OpenTelemetry, Prometheus, plus LLM-specific tracing (Langfuse or equivalent), token/cost metrics, and eval pipelines.
* Define SLOs/SLIs and error budgets for AI services - latency, model fallback chains, eval regression gates, agent success rates. Lead incident readiness, response, and post-mortems.
* Drive FinOps: model routing by cost, cache hit rates, batch vs. realtime tradeoffs, budget alarms, per-team chargeback visibility.
* Implement guardrails: prompt-injection defenses, PII redaction, model allowlists, human-in-the-loop checkpoints, audit trails. Org Impact
* Identify high-leverage workflows across the organization and translate them into scalable agentic automations.
* Partner with R&D, Delivery, security, and external vendors to deliver platform capabilities.

About Alice:
Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact—whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection.

Hybrid:
Yes

Required Computational Linguist (NLU)
If youre the one - youll be working on the most incredible sales-assistant in the industry.

Required Senior ML Engineer
Responsibilities
As a Senior Machine Learning engineer, you will be working directly with our Head of R&D and will take a major role building our Pragmatic Intelligence Engine using and experimenting with S.O.T.A Deep Learning architectures, including: Multi-Layer Perceptron Neural Network, LSTM, mLSTM, CNN, RNN, VDCNN, etc.

In your role as a WiFi ML Link Adaptation Algorithm Engineer, as part of our Connectivity Group, you will be part of a world-class group that pioneers design and development of MAC Layer algorithms for wireless communication systems on our products.

We are looking for ML algorithm engineer to innovate and develop advanced rate adaptation and link optimization algorithms for WiFi systems. You will design and develop intelligent algorithms that dynamically optimize wireless transmission parameters to maximize throughput and reliability across diverse channel conditions.

Responsibilities:
Develop algorithms and models to improve rate selection, frame sizes, antenna selection and more with using ML.
Build/Configure simulation environments modeling fading channels, interference, and multi-user scenarios.
Validate performance of algorithms and models in network and HW simulations across diverse channel conditions.
Convert models and algorithms to embedded firmware implementation.
Create comprehensive test frameworks to evaluate real-world performance.
Conduct over-the-air testing and performance characterization on devices.
Analyze field telemetry data to identify optimization opportunities.
Debug and resolve algorithm-related issues in real-world deployment scenarios.

we are looking for a Senior Machine Learning Scientist I.
As a Senior Machine Learning Scientist, you will work closely with engineers and to design, develop, and evaluate machine learning solutions for scalable, customer-facing GenAI applications. Your work will focus on researching, training, fine-tuning, and rigorously evaluating models leveraging LLMs, recommendation systems, and agent-based architectures, using state-of-the-art techniques. You will drive experimentation, define success metrics, and translate insights into impactful AI solutions that shape the future of intelligent travel products.
Key Job Responsibilities and Duties:
Explore and apply state-of-the-art techniques in multimodal machine learning.
Train innovative ML models (NLP, CV, LLM-finetuning), build algorithms, and engineering approaches to drive business impact..
Coding skills: ensure implementation of reusable frameworks (clean and scalable code).
Conduct data analysis with detailed metrics to evaluate models performance, labels quality, features exploration.
Work closely with machine learning engineers to ensure the model's latency/throughput meets product requirements and ensure deployment of your model to production.
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.